This paper presents a novel approach for texture classification, generalizing the wellknown local binary patterns (LBP). In the proposed approach, two different and complementary types of features are extracted from local patches, based on pixel intensities and differences. Inspired by the LBP approach, two intensity-based and two difference-based descriptors are developed. All four descriptors have the same form as the conventional LBP codes, thus they can be readily combined to form joint histograms to represent textured images. The proposed approach is computationally simple and is training-free: there is no need to learn a texton dictionary and no tuning of parameters. Extensive experimental results on two challenging texture databases (Outex and KTHTIPS2b) show that the proposed approach significantly outperforms the classical LBP approach and other state-of-the-art methods with a nearest neighbor classifier.